4 papers
Free Energy Universality in Tensor Estimation via Generic Chaining
Wenxuan Zou, Galen Reeves
We study high-dimensional inference problems with tensor-structured data and establish conditions under which their free energy can be approximated by that of a Gaussian comparison…
Linear Operator Approximate Message Passing (OpAMP)
Riccardo Rossetti, Bobak Nazer, Galen Reeves
This paper introduces a framework for approximate message passing (AMP) in dynamic settings where the data at each iteration is passed through a linear operator. This framework is…
What happens when generative AI models train recursively on each others' outputs?
Hung Anh Vu, Galen Reeves, Emily Wenger
The internet serves as a common source of training data for generative AI (genAI) models but is increasingly populated with AI-generated content. This duality raises the possibilit…
Statistical Limits for Finite-Rank Tensor Estimation
Riccardo Rossetti, Galen Reeves
This paper provides a unified framework for analyzing tensor estimation problems that allow for nonlinear observations, heteroskedastic noise, and covariate information. We study a…